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During the last decades, the production enterprises have gone through a strong global change in terms of shorter product life cycles, fluctuations in the order income and increased demand of customized products. Basically, a company needs to develop appealing products in terms of cost and quality that are brought to the market in timely manner. As many studies show that over 70% of the total life cycle cost of a product is determined at the early design stage, this thesis work are focused on analyzing how the total cost of robot families can be affected in the early design stage through changing the component commonality level. More specifically, a cost estimation model in excel has been built to see how the total costs of robot family IRB 6640 are affected when choosing different gears for joints one, two and three. Also, a more general analysis has been done where it is investigated how ABB can take benefit of a product configuration system integrated with a robot platform and cost estimation model. The result of this study shows that the traditional opinion on " higher commonality means lower costs " is not applicable in all cases. For instance, considering the commonality of gears within a robot family, the optimal solution out of a cost perspective do no longer exists at the highest commonality possible but at a slightly lower commonality level, lying between 0,7<CI<0,9 using the measurement commonality index (CI). This is because the gears tend to be over dimensioned, and thereby more expensive for certain joints when commonality increases. The analysis also shows that fix and variable costs are not linear to each other, which complicates the situation when trying to describe the change of total costs with one commonality index. Consequently, two different commonality indices are needed: CI to describe the fix costs and CIC (component part commonality index) to describe the variable costs. Cost analysis of robot families 4 5 Preword With these words, the writer would like to thank especially Marcus Pettersson (supervisor from ABB Corporate Research) for the information and support needed to accomplish this thesis work. Also many thanks to Johan Ölvander (supervisor from Linköpings Universitet), Xiaolong Feng (project leader at ABB Corporate Research) and Leif Pind (ABB robotics).